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Hello I have an hourly timeseries that I would like to mask if the timeseries index is outside business hours.

I can achieve what I want for business day data but not hourly data

import datetime
import pandas as pd
import numpy as np
from pandas.tseries.offsets import *

st = datetime.datetime(2013, 1, 1)
ed = datetime.datetime(2013, 2, 1)
myrange = pd.date_range(st, ed, freq='H')
ts = pd.Series(np.random.randn(len(myrange)), index=myrange)
ts.asfreq(BDay()).asfreq(Day())

I have tried generating a BDay date range and then changing the freq to hourly but this doesn't work.

newrange = pd.date_range(datetime.datetime(2013, 1, 1), datetime.datetime(2013, 1, 1), freq='B') 
#but adding this doesn't work .asfreq(Hour())
ts[ts.index.isin(newrange)].asfreq(Hour()) #Of course this only gives one value on the day

Thanks

Katie Hurley
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bevanj
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1 Answers1

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To restrict your times to Business days you could use:

ts = ts.ix[ts.index.map(BDay())]

and indexer_between_time to restrict between business hours:

ts = ts.ix[ts.index.indexer_between_time(time(7), time(18))]

To restrict to Business days within business hours (apply these in either order).

Community
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Andy Hayden
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